Updates

Model and report changes

  1. Over January 2022 our model was redesigned to incorporate the booster vaccination campaign and to be able to capture, in an unbiased way, the ecological effects of the emergence and subsequent dominance of the omicron variant. In particular, we have stratified the entire model by vaccination status so that we can more accurately capture the impacts of waning immunity. The duration of immunity following infection is assumed to have a mean of around 18 months, but this falls rapidly around the time of the emergence of Omicron to account for the immune escape of this variant. The waning of vaccine-derived immunity is captured through an assumed drop in vaccine efficacy at this time.
  2. A further modification to the model has been required due to the scaling back of the testing programme. Since March 2020 we have been calibrating the model against reported deaths within sixty days of a positive test. We now report the results from a model that uses data on new diagnoses in hospitals (where testing is still routine). The structure of the model remains similar but we make new assumptions about the proportion of infections that are detected by hospital-based testing, and the time taken post-infection for this detection to occur.
  3. We have extended the use of serological data to use samples taken beyond the first wave of the pandemic. The samples are those collected by NHS Blood and Transplant using the Roche-N assay, which measures the prevalence of infection-acquired antibodies in the population.
  4. The model also accounts for a different susceptibility to infection in each adult age group (no prior information is used); and for the under-15s, (using prior information from Viner et al, 2020, which estimates children to be less likely to acquire infection when in contact with an infectious individual).
  5. The model has the ability to incorporate estimates of community prevalence, by region and age group, from the Office of National Statistics Coronavirus (COVID-19) Infection Survey (ONS CIS; see Data Sources for details). These are included weekly since the outset of the Survey in May 2020 for the age groups >4 years to provide a more timely source of information on recent trends in incidence.
  6. The underlying probability of an unvaccinated individual testing positive in hospital following infection with SARS-CoV2 (the apparent infection-hospitalisation rate, IHR) is allowed to change gradually over the course of 30 days every (approximately) 100 days. This is designed to reflect fluctuations due to seasonal effects, demand on healthcare services or the emergence of new virus variants of differing severity.
  7. The ‘Epidemic summary’ only reports the current value for the IHR by age. To visualise how this has changed over time in our model, see the IHR tab in the ‘Infections and Deaths’ section of the report. The quantity that is now plotted under this tab is the probability of being diagnosed in hospital if infected, taking into account the impact of the immunisation programme - it is an average of a lower rate of hospitalisation among vaccinated individuals and a higher rate among the unvaccinated.
  8. The attack rate table shows the proportion of individuals to have ever been infected over the course of the pandemic. Under any `Infections’ tab, what is presented is the totality of all infections, including reinfections, and will therefore show values higher than the attack rate might suggest.

Updated findings

  1. The estimated number of new daily infections on 9th July across England has not changed significantly from last week and is now 295,000 (259,000--335,000, 95% credible interval) infections per day, corresponding to a national daily infection rate of 528 per 100k population. The highest rate is currently in the North West (NW) with 674 infections per 100K population, slightly ahead of Yorkshire and The Humber (YH) with 664. The next highest rates are to be found in the West Midlands (WM), South East (SE) and South West (SW) with 591, 534 and 512 infections per 100K population respectively, all above the national average. Greater London (GL) and East of England (EE) have infection rates of over 400 per 100k, whilst the lowest rates are estimated to be in the East Midlands (EM) and the North East (NE), though even here the rates are above 350 infections per 100k. Note that a substantial proportion of these infections will be asymptomatic.
  2. The model predictions for the daily number of new diagnoses in hospitals reached a low of 350 per day around May 28th and has increased sharply since, though its growth is estimated to be slowing, currently doubling in number every 29.3 days across the country. We forecast that by July 30th, there will be 1,800 to 2,400 new diagnoses in hospitals per day, having hit a peak around July 19th, a shift of two days from last week’s estimate.
  3. The infection growth rate for England is 0.01 (0.01-- 0.02) per day. This means that, nationally, the number of infections is increasing, corresponding to an Rt of around 1.13.
  4. Though the national Rt is likely to be greater than 1, we estimate the epidemic to be at, or close to, a peak. The reproduction number is currently highest in the SE and YH.
  5. Our estimates for the attack rate, the proportion of a population who have ever been infected, is up to 86.4% nationwide, and is over 90% in two regions, NE and WM. However, the estimated total number of infections to date (69.3m) far exceeds the size of the population of England, due to the presence of reinfections.
  6. Note that the hospitalised diagnosis data used are only partially informative on Rt over the last two weeks. Therefore, the estimate for current incidence, Rt and the forecast of daily numbers of deaths are likely to be subject to significant revision.

Interpretation

Across England, we estimate that the daily number of diagnoses in hospitals remains on the increase. PCR-positive infection prevalence is estimated to be similarly increasing in all ONS regions except the NE and in all age groups. Despite the high rates of infection over the Omicron waves, continued waning of immunity and the increasing presence of the BA.4 and BA.5 sub-strains are sustaining a national Rt that is currently around 1.1.

Overall, since December 1st 2021, the Omicron variant has resulted in the cumulative number of infections increasing from 25.8 million individuals to 69.3 million. In total, 30% of all infections over the course of the entire pandemic are re-infections and this fraction is only expected to increase.

During the Omicron-era, December 2021-May 2022, we estimate a fall of over 75% in the IHR in the age groups over 45 and a decline of over 50% in the under 45 age groups. Since May, we estimate an increase to a pre-Omicron level in the under-45s, with small increases in the 45-64 and 65-74 age groups, whilst for the over-75s there is still evidence of an improving IHR. This phenomenon in the under-45s could, in part, be explained by a reduced protection from the vaccines against severe symptoms due to infection with BA.4 and BA.5. Additionally, the model does not yet include the second booster vaccinations that the over-75 age group have received, so any consequent improvement in health outcomes will be attributed to a drop in IHR. The overall IHR increases only modestly over this most recent, from 0.7% to 0.9%. The over-75s still have the highest IHR at 3.1% (2.9%--3.4%).

Summary

Real-time tracking of an epidemic, as data accumulate over time, is an essential component of a public health response to a new outbreak. A team of statistical modellers at the MRC Biostatistics Unit (BSU), University of Cambridge, are working to provide regular now-casts and forecasts of COVID-19 infections and deaths. This information feeds directly to the SAGE sub-group, Scientific Pandemic Influenza sub-group on Modelling (SPI-M), and to regional Department of Health and Social Care (DHSC) teams.

Methods

We fit a transmission model (Birrell et al. 2020) to a number of data sources (see 'Data Sources'), to reconstruct the number of new COVID-19 infections over time in different age groups and ONS regions, estimate a measure of ongoing transmission and predict the number of new hospital admissions associated with COVID-19.

Data sources

We use:

  1. Data on COVID-19 confirmed deaths from the Public Health England (PHE) line-listing This consists of a combination of deaths notified to:
    • the Demographics Batch Service (DBS), a mechanism that allows PHE to submit a file of patient information to the National Health Service spine for tracing against the personal demographics service (PDS). PHE submit a line list of patients diagnosed with COVID-19 to DBS daily. The file is returned with a death flag and date of death updated (started 20th March, 2020).
    • NHS England, who report data from NHS trusts relating to patients who have died after admission to hospital or within emergency department settings.
    • Health Protection Teams (HPTs), resulting from a select survey created by PHE to capture deaths occurring outside of hospital settings, e.g. care homes (started 23rd March, 2020)
  2. Data on antibody prevalence in blood samples from a PHE survey of NHS Blood Transfusion (NHSBT) donors.

Data are stratified into eight age groups: <1, 1-4, 5-14, 15-24, 25-44, 45-64, 65-74, 75+, and the ONS regions, similar to government office regions (North East, Yorkshire and Humberside, North West, East Midlands, West Midlands, East of England, London, South East, South West).

  1. Published information on the the natural history of COVID-19 (Verity et al., 2020; Li et al, 2020)
  2. Information on contacts between different age groups from:
    • A Survey that describes relative rates of contacts between different age groups (Mossong et al. 2008).
    • Google Community Mobility reports, informing the changes in people’s mobility over the course of the pandemic, particularly after the March 23rd lockdown measures.
    • The ONS’ time use survey, which in conjunction with the google mobility study, allows estimation of the changing exposure to infection risk over time.
    • Data from the Department for Education describing the proportion of children currently attending school.
  3. Daily data on the numbers of people getting immunised by age-group and region. These data are derived from the National Immunisation Management Service (NIMS). These data includes all COVID-19 immunisations administered at hospital hubs, local immunisation service sites such as GP practices, and dedicated immunisation centres.
  4. Data on new diagnoses in hospital testing. This is a composite dataset derived from two sources:
    • February-October 2020: The NHS England Secondary Uses Service (SUS) dataset, a collection of complete, accurate information on all hospitalisations with and due to COVID-19 infection. This is a comprehensive dataset but lacks the timeliness required for real-time surveillance, as individual records are completed for an individual and recorded on the system only upon death or hospital discharge.
    • October 2020 onwards: NHS England and NHS Improvement COVID-19 Hospital Daily Situation Reports. Prior to this date, the NHS Situation Reports did not have the age-group granularity required by the model.

Epidemic summary

Current \(R_t\)

Value of \(R_t\), the average number of secondary infections due to a typical infection today.

Number of infections

Attack rate

The percentage of a given group that has been infected.

By region

By age

Current IHR

Change in infections incidence

Growth rates

NB: negative growth rates are rates of decline. Values are daily changes.

Region Median 95% CrI (lower) 95% CrI (upper)
England 0.01 0.01 0.02
East of England 0.01 -0.01 0.02
East Midlands 0.00 -0.02 0.03
London 0.00 -0.02 0.02
North East -0.02 -0.05 0.00
North West 0.01 -0.01 0.03
South East 0.02 0.00 0.04
South West 0.01 -0.01 0.03
West Midlands 0.01 -0.01 0.02
Yorkshire and The Humber 0.03 0.01 0.04

Halving times

Halving times in days, if a region shows growth than value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England NA NA NA
East of England NA 46.91 NA
East Midlands NA 29.39 NA
London NA 36.12 NA
North East 32.71 14.73 578.3
North West NA 122.84 NA
South East NA 569.66 NA
South West NA 97.27 NA
West Midlands NA 68.63 NA
Yorkshire and The Humber NA NA NA

Doubling times

Doubling times in days, if a region shows decline then the value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England 52.60 34.26 124.50
East of England 118.40 27.86 NA
East Midlands 157.95 25.42 NA
London 146.59 31.60 NA
North East NA NA NA
North West 56.50 22.29 NA
South East 36.68 19.14 NA
South West 49.26 21.61 NA
West Midlands 88.91 33.08 NA
Yorkshire and The Humber 26.88 17.44 107.12

Change in admissions incidence

Growth rates

NB: negative growth rates are rates of decline. Values are daily changes.

Region Median 95% CrI (lower) 95% CrI (upper)
England 0.02 0.02 0.03
East of England 0.02 -0.01 0.04
East Midlands 0.01 -0.01 0.04
London 0.02 -0.01 0.03
North East -0.01 -0.03 0.01
North West 0.03 0.01 0.05
South East 0.03 0.01 0.05
South West 0.03 0.00 0.05
West Midlands 0.03 0.01 0.04
Yorkshire and The Humber 0.04 0.02 0.06

Halving times

Halving times in days, if a region shows growth than value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England NA NA NA
East of England NA 119.62 NA
East Midlands NA 62.16 NA
London NA 85.99 NA
North East 104.55 24.87 NA
North West NA NA NA
South East NA NA NA
South West NA NA NA
West Midlands NA NA NA
Yorkshire and The Humber NA NA NA

Doubling times

Doubling times in days, if a region shows decline then the value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England 29.30 22.46 42.18
East of England 45.41 18.69 NA
East Midlands 49.85 17.27 NA
London 44.69 20.05 NA
North East NA 51.64 NA
North West 25.65 14.00 80.90
South East 23.51 13.97 75.57
South West 27.09 14.67 255.97
West Midlands 27.08 16.13 105.54
Yorkshire and The Humber 17.72 11.53 41.52 
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Infections and hospitalisations

The shaded areas show periods of national lockdown, the green lines the dates (once confirmed) of the steps in the roadmap in the UK Governement's COVID-19 Response -- Spring 2021, and the red line shows the date these results were produced (09 Jul).

Infection incidence

By region

By age

Cumulative infections

By region

By age

Admissions incidence

By region

By age

Cumulative admissions

By region

By age

IHR

Prob \(R_t > 1\)

The figure below shows the probability that \(R_t\) is greater than 1 (ie: the number of infections is growing) in each region over time. Clicking the regions in the legend allows lines to be added or removed from the figure.

\(R_t\)

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